Differential phospho/protein analysis - CRC Batch 1
Sophie Herbst
Analysis date: 2023-02-10
Depends on
DIPG_FirstBatch_DataProcessing Script
load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")TODO
- Do differential abudance analysis for prep batch and mass spec run
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY")## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the plotly package.
## Please report the issue at <]8;;https://github.com/plotly/plotly.R/issueshttps://github.com/plotly/plotly.R/issues]8;;>.Return_DEP_Hits_Plots(data = pY_Set2_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(comparison)
##
## # Now:
## data %>% select(all_of(comparison))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.8604207
## 2: ABC transporter disorders 0.3480392
## 3: ABC-family proteins mediated transport 0.2157895
## 4: ADP signalling through P2Y purinoceptor 1 0.7151052
## 5: ALK mutants bind TKIs 0.3688363
## 6: APC/C-mediated degradation of cell cycle proteins 0.1157895
## padj log2err ES NES size leadingEdge
## 1: 0.9665167 0.05049830 0.4661458 0.6940019 2 6385,1464
## 2: 0.8984150 0.11237852 -0.5432428 -1.1208733 5 5696,5687,5692,5694
## 3: 0.8226559 0.15315881 -0.5602097 -1.2386975 6 5696,5687,1965,5692,5694
## 4: 0.9665167 0.05909548 0.5600116 0.8337500 2 1432,6714
## 5: 0.9034388 0.09528798 0.8337662 1.1032501 1 1213
## 6: 0.8043813 0.21392786 -0.6286041 -1.3899265 6 983,5696,5687data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.3234624
## 2: ABC transporter disorders 0.3828125
## 3: ABC-family proteins mediated transport 0.4647676
## 4: ADP signalling through P2Y purinoceptor 1 0.8883827
## 5: ALK mutants bind TKIs 0.6749049
## 6: APC/C-mediated degradation of cell cycle proteins 0.3133433
## padj log2err ES NES size leadingEdge
## 1: 0.6800285 0.11237852 0.6796875 1.0967802 2 1464,6385
## 2: 0.6975069 0.08020234 -0.5289330 -1.0595494 5 5692,5696,5693,5687
## 3: 0.7576935 0.06815134 -0.4761114 -1.0062663 6 5692,5696,5693,5687,1965
## 4: 0.9367712 0.05712585 0.4401042 0.7101757 2 1432,6714
## 5: 0.8551070 0.06157068 0.6649351 0.8835653 1 1213
## 6: 0.6800285 0.08889453 -0.5325901 -1.1256346 6 5692,983,5696,5693,5687Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2043011
## 2: ABC transporter disorders 0.4234568
## 3: ABC-family proteins mediated transport 0.5950992
## 4: ADP signalling through P2Y purinoceptor 1 0.9207607
## 5: ALK mutants bind TKIs 0.2625000
## 6: APC/C-mediated degradation of cell cycle proteins 0.2357060
## padj log2err ES NES size leadingEdge
## 1: 0.5304528 0.15964670 0.7317708 1.2440878 2 1464
## 2: 0.6651280 0.06321912 -0.5071326 -1.0345363 5 5693,5696,5692
## 3: 0.7900302 0.04477489 -0.4241333 -0.9098507 6 5693,5696,5692
## 4: 0.9733705 0.03879622 -0.4322917 -0.6811524 2 6714,1432
## 5: 0.5746246 0.12043337 -0.8779221 -1.1654585 1 1213
## 6: 0.5470826 0.09082414 -0.5568822 -1.1946236 6 983,5693,5696,5692EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, additional_title = "pY", contrast = "EC_vs_E", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.4781421
## 2: ABC transporter disorders 0.4330025
## 3: ABC-family proteins mediated transport 0.5592814
## 4: ADP signalling through P2Y purinoceptor 1 0.7755102
## 5: ALK mutants bind TKIs 0.2821497
## 6: APC/C-mediated degradation of cell cycle proteins 0.7293413
## padj log2err ES NES size leadingEdge
## 1: 0.7855344 0.09923333 0.5703125 0.9629962 2 1464,6385
## 2: 0.7499462 0.06238615 -0.5436825 -1.0406948 5 5692,5693
## 3: 0.8169538 0.04879897 -0.4719043 -0.9484551 6 5692,5693
## 4: 0.9221278 0.04623025 -0.5208333 -0.7943202 2 6714,1432
## 5: 0.6722759 0.11012226 -0.8545455 -1.1385447 1 1213
## 6: 0.9054648 0.03660822 -0.4094728 -0.8229774 6 5692,5693
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.2646240
## 2: ABC transporter disorders 0.4969988
## 3: ABC-family proteins mediated transport 0.6323185
## 4: ADP signalling through P2Y purinoceptor 1 0.8245342
## 5: ALK mutants bind TKIs 0.1710262
## 6: APC/C-mediated degradation of cell cycle proteins 0.3325527
## padj log2err ES NES size leadingEdge
## 1: 0.7624862 0.14122512 0.7187500 1.1637136 2 1464,6385
## 2: 0.8115798 0.05434344 -0.5069814 -0.9879145 5 5693,5687,5696
## 3: 0.8994749 0.04216194 -0.4390481 -0.8957048 6 5693,5687,5696
## 4: 0.9446513 0.04293111 -0.5026042 -0.7693801 2 6714,1432
## 5: 0.5935614 0.15016980 -0.9220779 -1.2273976 1 1213
## 6: 0.8115798 0.07253519 -0.5427770 -1.1073227 6 5693,983,5687,5696## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] forcats_0.5.2 stringr_1.4.1
## [3] dplyr_1.0.10 purrr_0.3.5
## [5] readr_2.1.3 tidyr_1.2.1
## [7] tibble_3.1.8 ggplot2_3.3.6
## [9] tidyverse_1.3.2 mdatools_0.13.0
## [11] SummarizedExperiment_1.24.0 GenomicRanges_1.46.1
## [13] GenomeInfoDb_1.30.1 MatrixGenerics_1.6.0
## [15] matrixStats_0.62.0 DEP_1.16.0
## [17] org.Hs.eg.db_3.14.0 AnnotationDbi_1.56.2
## [19] IRanges_2.28.0 S4Vectors_0.32.4
## [21] Biobase_2.54.0 BiocGenerics_0.40.0
## [23] fgsea_1.20.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 shinydashboard_0.7.2 proto_1.0.0
## [4] gmm_1.7 tidyselect_1.2.0 RSQLite_2.2.18
## [7] htmlwidgets_1.5.4 grid_4.1.3 BiocParallel_1.28.3
## [10] norm_1.0-10.0 munsell_0.5.0 codetools_0.2-18
## [13] preprocessCore_1.56.0 chron_2.3-58 DT_0.26
## [16] withr_2.5.0 colorspace_2.0-3 highr_0.9
## [19] knitr_1.40 rstudioapi_0.14 mzID_1.32.0
## [22] labeling_0.4.2 GenomeInfoDbData_1.2.7 pheatmap_1.0.12
## [25] bit64_4.0.5 farver_2.1.1 vctrs_0.5.0
## [28] generics_0.1.3 xfun_0.34 R6_2.5.1
## [31] doParallel_1.0.17 clue_0.3-62 MsCoreUtils_1.6.2
## [34] bitops_1.0-7 cachem_1.0.6 DelayedArray_0.20.0
## [37] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.1
## [40] googlesheets4_1.0.1 gtable_0.3.1 affy_1.72.0
## [43] sandwich_3.0-2 rlang_1.0.6 mzR_2.28.0
## [46] GlobalOptions_0.1.2 lazyeval_0.2.2 gargle_1.2.1
## [49] impute_1.68.0 broom_1.0.1 BiocManager_1.30.19
## [52] yaml_2.3.6 modelr_0.1.9 crosstalk_1.2.0
## [55] backports_1.4.1 httpuv_1.6.6 tools_4.1.3
## [58] affyio_1.64.0 ellipsis_0.3.2 gplots_3.1.3
## [61] jquerylib_0.1.4 RColorBrewer_1.1-3 STRINGdb_2.6.5
## [64] MSnbase_2.20.4 gsubfn_0.7 Rcpp_1.0.9
## [67] hash_2.2.6.2 plyr_1.8.7 zlibbioc_1.40.0
## [70] RCurl_1.98-1.9 sqldf_0.4-11 GetoptLong_1.0.5
## [73] zoo_1.8-11 haven_2.5.1 cluster_2.1.4
## [76] fs_1.5.2 magrittr_2.0.3 data.table_1.14.4
## [79] circlize_0.4.15 reprex_2.0.2 reactome.db_1.77.0
## [82] googledrive_2.0.0 pcaMethods_1.86.0 mvtnorm_1.1-3
## [85] ProtGenerics_1.26.0 hms_1.1.2 mime_0.12
## [88] evaluate_0.17 xtable_1.8-4 XML_3.99-0.12
## [91] readxl_1.4.1 gridExtra_2.3 shape_1.4.6
## [94] compiler_4.1.3 KernSmooth_2.23-20 ncdf4_1.19
## [97] crayon_1.5.2 htmltools_0.5.3 later_1.3.0
## [100] tzdb_0.3.0 lubridate_1.8.0 DBI_1.1.3
## [103] dbplyr_2.2.1 ComplexHeatmap_2.10.0 MASS_7.3-58.1
## [106] tmvtnorm_1.5 Matrix_1.5-1 cli_3.4.1
## [109] vsn_3.62.0 imputeLCMD_2.1 parallel_4.1.3
## [112] igraph_1.3.5 pkgconfig_2.0.3 plotly_4.10.0
## [115] MALDIquant_1.21 xml2_1.3.3 foreach_1.5.2
## [118] bslib_0.4.0 XVector_0.34.0 rvest_1.0.3
## [121] digest_0.6.30 Biostrings_2.62.0 rmarkdown_2.17
## [124] cellranger_1.1.0 fastmatch_1.1-3 shiny_1.7.3
## [127] gtools_3.9.3 rjson_0.2.21 lifecycle_1.0.3
## [130] jsonlite_1.8.3 viridisLite_0.4.1 limma_3.50.3
## [133] fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
## [136] KEGGREST_1.34.0 fastmap_1.1.0 httr_1.4.4
## [139] plotrix_3.8-2 glue_1.6.2 fdrtool_1.2.17
## [142] png_0.1-7 iterators_1.0.14 bit_4.0.4
## [145] stringi_1.7.8 sass_0.4.2 blob_1.2.3
## [148] caTools_1.18.2 memoise_2.0.1knitr::knit_exit()